Artificial intelligence (AI) and machine learning (ML) have been the subjects of increased interest in recent years due to their benefits across several fields. One sector that can benefit from these tools is the tribology industry, with an emphasis on friction and wear prediction. This industry hopes to train and utilize AI algorithms to classify equipment life status and forecast component failure, mainly using supervised and unsupervised learning approaches. This article examines some of the methods that have been used to accomplish this, such as condition monitoring for predictions in material selection, lubrication performance, and lubricant formulation. Furthermore, AI and ML can support the determination of tribological characteristics of engineering systems, allowing for a better fundamental understanding of friction, wear, and lubrication mechanisms. Moreover, the study also finds that the continued use of AI and ML requires access to findable, accessible, interoperable, and reusable data to ensure the integrity of the prediction tools. The advances of AI and ML methods in tribology show considerable promise, providing more accurate and extensible predictions than traditional approaches.